Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (1): 10-16.doi: 10.6040/j.issn.1672-3961.0.2017.419

• Machine Learning & Data Mining • Previous Articles     Next Articles

Face recognition based on improved prameter-free supervised localitypreserving projections

Jun FAN1,2(),Qiaolin YE1,Ning YE1,*()   

  1. 1. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
    2. School of Civil Engineering, Jiangsu College of Engineering and Technology, Nantong 226007, Jiangsu, China
  • Received:2017-08-24 Online:2019-02-20 Published:2019-03-01
  • Contact: Ning YE E-mail:13773679049@139.com;njfuyn@163.com
  • Supported by:
    江苏省高等职业院校国内高级访问学者计划(2016GRFX013);江苏省青蓝工程培养对象(2016-15);江苏省住建厅建设系统科技计划(2017ZD138);江苏省高校哲学社会科学研究(2018SJA1247);校科研计划(GYKY/2017/5, GYKY/2017/12)

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Abstract:

To solve the problem of unsupervised and complexity of parameter selection of the locality preserving projection algorithm, an improved supervised dice parameter-free locality preserving projection algorithm (SdPLPP)was proposed. SdPLPP constructed affinity matrix by using generalized Dice coefficient and extract features of data under the supervised mode, which could avoid the problems of parameters selection and adjustment of locality preserving projection (LPP) algorithm. The proposed algorithm performed experiment of image visualization based on the Iris dataset, analyzed the relationship between the value of the distance of sample data and the performance of the algorithm. To verifying the effectiveness and performance of algorithm, SdPLPP carried out the feature extraction experiments based on three kinds of human face databases, such as ORL, Yale and FERET, and used nearest neighbor classifier to get correct recognition rate. The experimental results showed that the SdPLPP algorithm was superior to PCA, ULDA, LPP, SPLPP and EP-SLPP algorithm in face recognition, and it was better than other algorithms of supervised parameter-free locality preserving projections.

Key words: feature extraction, locality preserving projections (LPP), parameter-free affninty matrix, generalized Dice coefficient, face recognition

CLC Number: 

  • TP391.4

Fig.1

Performance analysis of image visualization algorithm"

Table 1

Comparison of Iris data projection distance of within class & between class in different algorithms"

cm
算法 M1 M2 M3 Sw23 Sb23 r23
PCA 0.54 0.77 0.92 0.84 1.56 1.84
ULDA 0.14 0.13 0.17 0.15 0.17 1.13
LPP 0.49 0.74 0.88 0.81 1.64 2.03
SPLPP 0.39 0.39 0.44 0.41 0.80 1.93
EP-SLPP 0.35 0.35 0.45 0.40 0.94 2.35
SdPLPP 0.32 0.59 0.68 0.63 1.64 2.58

Table 2

Comparison of top recognition accuracy of differentalgorithms in ORL (32×32) face database"

%
算法 训练集个数
2 3 4 5
PCA 68.40 78.25 83.13 87.90
ULDA 79.47 86.46 91.08 93.95
LPP 67.53 77.43 82.25 87.05
SPLPP 71.44 81.36 85.08 89.30
EP-SLPP 79.50 86.46 91.25 94.05
SdPLPP 83.66 91.07 94.33 96.50

Fig.2

Relationship between the average recognition accuracy and projection vector of different algorithms in ORL (64×64) face database"

Table 3

Comparison of top recognition accuracy of differentalgorithms in Yale (32×32) face database"

%
算法 训练集个数
2 3 4 5
PCA 42.15 48.83 52. 67 56.11
ULDA 56.00 66.83 74.57 77.44
LPP 41.78 49.25 52.29 55.11
SPLPP 42.96 50.25 53.14 56.89
EP-SLPP 54.52 66.75 74.86 78.78
SdPLPP 55.04 66.92 75.90 80.11

Table 4

Training dataset setting method of FERET facesub-database"

训练集 Test1 Test2 Test3 Test4
组合方式 Ia+Ib+Id Ia+Ib+Ie Ia+Ic+Id Ia+Ic+Ie

Table 5

Comparison of top recognition accuracy of differentalgorithms in FERET(80×80) face database"

%
算法 训练集
Test1 Test2 Test3 Test4
PCA 78.25 81.75 70.75 73.50
ULDA 78.17 85.83 74.67 83.67
LPP 78.75 81.75 71.00 74.00
SPLPP 78.75 82.00 70.75 74.00
EP-SLPP 88.33 92.33 80.67 89.17
SdPLPP 86.50 90.67 82.50 89.67
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